International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Predictive Analysis for Big Mart Sales Using Machine Learning Algorithms

Abstract

Currently, supermarket run-centres, Big Marts keep track of each individual item's sales data in order to anticipate potential consumer demand and update inventory management. Anomalies and general trends are often discovered by mining the data warehouse's data store. For retailers like Big Mart, the resulting data can be used to forecast future sales volume using various machine learning techniques like big mart. A predictive model was developed using Decision Tree Regression for forecasting the sales of a business such as Big -Mart, and it was discovered that the model outperforms existing models.

Country : India

1 Alphan Shaikh2 Mohd Khalid Shaikh3 Ayaan Sayyed4 Prof. A.N.Gedam

  1. Student, Computer Engineering, AISSMS College of Polytechnic, Pune, Maharashtra, India
  2. Student, Computer Engineering, AISSMS College of Polytechnic, Pune, Maharashtra, India
  3. Student, Computer Engineering, AISSMS College of Polytechnic, Pune, Maharashtra, India
  4. Asst. Professor, Computer Engineering, AISSMS College of Polytechnic, Pune, Maharashtra, India

IRJIET, Volume 9, Issue 3, March 2025 pp. 326-331

doi.org/10.47001/IRJIET/2025.903047

Full Paper
Download

References

  1. Ching Wu Chu and Guoqiang Peter Zhang, “A comparative study of linear and nonlinear models for aggregate retails sales forecasting”, Int. Journal Production Economics, vol. 86, pp. 217- 231, 2003.
  2. Wang, Haoxiang. "Sustainable development and management in consumer electronics using soft computation." Journal of Soft Computing Paradigm (JSCP) 1, no. 01 (2019): 56.- 2.
  3. Suma, V., and Shavige Malleshwara Hills. "Data Mining based Prediction of Demand in Indian Market for Refurbished Electronics." Journal of Soft Computing Paradigm (JSCP) 2, no. 02 (2020): 101- 110
  4. Giuseppe Nunnari, Valeria Nunnari, “Forecasting Monthly Sales Retail Time Series: A Case Study”, Proc. of IEEE Conf. on Business Informatics (CBI), July 2017.
  5. O. Ajao Isaac, A. Abdullahi Adedeji, I. Raji Ismail, “Polynomial Regression Model of Making Cost Prediction In Mixed Cost Analysis”, Int. Journal on Mathematical Theory and Modeling, vol. 2, no. 2, pp. 14 – 23, 2012.
  6. Xinqing Shu, Pan Wang, “An Improved Adaboost Algorithm based on Uncertain Functions”, Proc. of Int. Conf. on Industrial Informatics – Computing Technology, Intelligent Technology, Industrial Information Integration, Dec. 2015.
  7. A.S. Weigend and N. A. Gershenfeld, “Time series prediction: Forecasting the future and understanding the past”, Addison-Wesley, 1994.
  8. N. S. Arunraj, D. Ahrens, A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting, Int. J. Production Economics 170 (2015) 321-335P
  9. D. Fantazzini, Z. Toktamysova, Forecasting German car sales using Google data and multivariate models, Int. J. Production Economics 170 (2015) 97-135.
  10. X. Yua, Z. Qi, Y. Zhao, Support Vector Regression for Newspaper/Magazine Sales Forecasting, Procedia Computer Science 17 ( 2013) 1055–1062.
  11. E. Hadavandi, H. Shavandi, A. Ghanbari, An improved sales forecasting approach by the integration of genetic fuzzy systems and data clustering: a Case study of the printed circuit board, Expert Systems with Applications 38 (2011) 9392–9399.
  12. P. A. Castillo, A. Mora, H. Faris, J.J. Merelo, P. Garcia Sanchez, A.J. Fernandez-Ares, P. De las Cuevas, M.I. Garcia-Arenas, Applying computational intelligence methods for predicting the sales of newly published books in a real editorial business management environment, Knowledge-Based Systems 115 (2017) 133-151.
  13. R. J. Kuo, Tung Lai HU and Zhen Yao Chen “application of radial basis function neural networks for sales forecasting”, Proc. Of Int. Asian Conference on Informatics in control, automation, and robotics, pp. 325- 328, 2009.
  14. Suresh K and Praveen O, "Extracting of Patterns Using Mining Methods Over Damped Window," 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2020, pp. 235-241, DOI:10.1109/ICIRCA48905.2020.9182893.
  15. Shobha Rani, N., Kavyashree, S., & Harshitha, R. (2020). Object Detection in Natural Scene Images Using Thresholding Techniques. Proceedings of the International Conference on Intelligent Computing and Control Systems, ICICCS 2020, Iciccs, 509–515.
  16. https://www.kaggle.com/brijbhushannanda1979/bigmartsalesdata.

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies